Overview

Dataset statistics

Number of variables10
Number of observations4829
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.5 KiB
Average record size in memory34.0 B

Variable types

Numeric9
Categorical1

Warnings

msno has a high cardinality: 4778 distinct values High cardinality
num_25 is highly correlated with num_unqHigh correlation
num_50 is highly correlated with num_75High correlation
num_75 is highly correlated with num_50High correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_25 and 2 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
num_25 is highly correlated with num_50 and 2 other fieldsHigh correlation
num_50 is highly correlated with num_25High correlation
num_75 is highly correlated with num_25High correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_25 and 2 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 1 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
num_50 is highly correlated with num_25 and 2 other fieldsHigh correlation
num_100 is highly correlated with num_unqHigh correlation
num_25 is highly correlated with num_50 and 1 other fieldsHigh correlation
num_75 is highly correlated with num_50 and 2 other fieldsHigh correlation
num_985 is highly correlated with num_75High correlation
num_unq is highly correlated with num_50 and 3 other fieldsHigh correlation
num_985 is highly skewed (γ1 = 22.86078337) Skewed
msno is uniformly distributed Uniform
df_index has unique values Unique
num_25 has 1196 (24.8%) zeros Zeros
num_50 has 2235 (46.3%) zeros Zeros
num_75 has 2600 (53.8%) zeros Zeros
num_985 has 2537 (52.5%) zeros Zeros
num_100 has 192 (4.0%) zeros Zeros

Reproduction

Analysis started2023-05-18 18:12:04.032248
Analysis finished2023-05-18 18:12:12.234227
Duration8.2 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct4829
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2435742.384
Minimum517
Maximum4828877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2023-05-18T18:12:12.318670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum517
5-th percentile270323.6
Q11235466
median2449426
Q33630864
95-th percentile4595879
Maximum4828877
Range4828360
Interquartile range (IQR)2395398

Descriptive statistics

Standard deviation1382347.658
Coefficient of variation (CV)0.5675262159
Kurtosis-1.180608149
Mean2435742.384
Median Absolute Deviation (MAD)1195565
Skewness-0.01513463332
Sum1.176219997 × 1010
Variance1.910885049 × 1012
MonotonicityNot monotonic
2023-05-18T18:12:12.428603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
478131
 
< 0.1%
13541631
 
< 0.1%
21370501
 
< 0.1%
31015941
 
< 0.1%
18328551
 
< 0.1%
13720041
 
< 0.1%
3965211
 
< 0.1%
16600591
 
< 0.1%
19042821
 
< 0.1%
28424611
 
< 0.1%
Other values (4819)4819
99.8%
ValueCountFrequency (%)
5171
< 0.1%
10571
< 0.1%
56181
< 0.1%
58881
< 0.1%
75461
< 0.1%
94931
< 0.1%
104041
< 0.1%
128551
< 0.1%
130791
< 0.1%
132771
< 0.1%
ValueCountFrequency (%)
48288771
< 0.1%
48259301
< 0.1%
48244891
< 0.1%
48243301
< 0.1%
48242601
< 0.1%
48230571
< 0.1%
48222381
< 0.1%
48220481
< 0.1%
48200901
< 0.1%
48194121
< 0.1%

msno
Categorical

HIGH CARDINALITY
UNIFORM

Distinct4778
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
xCajYQxbS49eajWxKzqz334LwCJ9KfxrJd7UxNYhaIY=
 
2
svBSLpPM7bavZWC9PqWaFH2ggaLUB3v+hxuYaimR4bE=
 
2
ybcB3cPPYvNQYPPtXfwr7Kn2mKjTB8A76yr0xdgvpEE=
 
2
6unhde+Y+GQ3rF9ycEcUkCfaZ7KmjZPgH6rpklp4c/c=
 
2
qarZbGz3cWOBvfn6E0FdUFjD61PNamg2XHThFPBC170=
 
2
Other values (4773)
4819 

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters212476
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4727 ?
Unique (%)97.9%

Sample

1st rowZMqqE8u7R/12bWzAViCUlfyEZHrcw80XEsrQHU+dZUg=
2nd rowMDBdrLTDMSW6N5HBrQ5zMINuP+40NtsNIaczfyZk//M=
3rd rownXhDC780WU3nOGfeCjSmvHu2LtVD3MpJQjGljN7lxvg=
4th rowK5tYghuu/DYjBZZaH+hfT3hOo+Oq/SSQ08A7+EgjWq4=
5th rowAwebbTh1Flk5wlsSVzpqSt3b0CSdqemjXv53zdhiqWc=

Common Values

ValueCountFrequency (%)
xCajYQxbS49eajWxKzqz334LwCJ9KfxrJd7UxNYhaIY=2
 
< 0.1%
svBSLpPM7bavZWC9PqWaFH2ggaLUB3v+hxuYaimR4bE=2
 
< 0.1%
ybcB3cPPYvNQYPPtXfwr7Kn2mKjTB8A76yr0xdgvpEE=2
 
< 0.1%
6unhde+Y+GQ3rF9ycEcUkCfaZ7KmjZPgH6rpklp4c/c=2
 
< 0.1%
qarZbGz3cWOBvfn6E0FdUFjD61PNamg2XHThFPBC170=2
 
< 0.1%
fidtcGs0Se8+L+vu5U5xn0CO49IF2JhUOkZGoSdY80M=2
 
< 0.1%
6V3gtsS1+lLYYpOSHQqIReGrpMoerNiA2SUmNEcg5M8=2
 
< 0.1%
0bNov9ubohMPwSlfi4SDwXG7hsNlxowIo9uZFZwBzj4=2
 
< 0.1%
MNYQViUgJh/ZnE256u+Klgu0UrEF/367nEDidkPYUfA=2
 
< 0.1%
pf9sgISBlORc9yqsD9+zU00MHf6Tw95EqciYtgm6YUQ=2
 
< 0.1%
Other values (4768)4809
99.6%

Length

2023-05-18T18:12:12.633392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xcajyqxbs49eajwxkzqz334lwcj9kfxrjd7uxnyhaiy2
 
< 0.1%
svbslppm7bavzwc9pqwafh2ggalub3v+hxuyaimr4be2
 
< 0.1%
ybcb3cppyvnqypptxfwr7kn2mkjtb8a76yr0xdgvpee2
 
< 0.1%
6unhde+y+gq3rf9ycecukcfaz7kmjzpgh6rpklp4c/c2
 
< 0.1%
qarzbgz3cwobvfn6e0fdufjd61pnamg2xhthfpbc1702
 
< 0.1%
fidtcgs0se8+l+vu5u5xn0co49if2jhuokzgosdy80m2
 
< 0.1%
6v3gtss1+llyyposhqqiregrpmoernia2sumnecg5m82
 
< 0.1%
0bnov9ubohmpwslfi4sdwxg7hsnlxowio9uzfzwbzj42
 
< 0.1%
mnyqviugjh/zne256u+klgu0uref/367nedidkpyufa2
 
< 0.1%
pf9sgisblorc9yqsd9+zu00mhf6tw95eqciytgm6yuq2
 
< 0.1%
Other values (4768)4809
99.6%

Most occurring characters

ValueCountFrequency (%)
=4829
 
2.3%
03604
 
1.7%
U3584
 
1.7%
Y3570
 
1.7%
E3543
 
1.7%
M3528
 
1.7%
83484
 
1.6%
A3475
 
1.6%
s3475
 
1.6%
c3456
 
1.6%
Other values (55)175928
82.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter84878
39.9%
Lowercase Letter84246
39.6%
Decimal Number32356
 
15.2%
Math Symbol7901
 
3.7%
Other Punctuation3095
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U3584
 
4.2%
Y3570
 
4.2%
E3543
 
4.2%
M3528
 
4.2%
A3475
 
4.1%
I3432
 
4.0%
Q3401
 
4.0%
H3286
 
3.9%
B3245
 
3.8%
O3237
 
3.8%
Other values (16)50577
59.6%
Lowercase Letter
ValueCountFrequency (%)
s3475
 
4.1%
c3456
 
4.1%
w3452
 
4.1%
g3449
 
4.1%
o3444
 
4.1%
k3405
 
4.0%
y3281
 
3.9%
r3256
 
3.9%
m3253
 
3.9%
i3240
 
3.8%
Other values (16)50535
60.0%
Decimal Number
ValueCountFrequency (%)
03604
11.1%
83484
10.8%
43422
10.6%
23192
9.9%
33187
9.8%
63161
9.8%
13130
9.7%
93066
9.5%
73062
9.5%
53048
9.4%
Math Symbol
ValueCountFrequency (%)
=4829
61.1%
+3072
38.9%
Other Punctuation
ValueCountFrequency (%)
/3095
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin169124
79.6%
Common43352
 
20.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U3584
 
2.1%
Y3570
 
2.1%
E3543
 
2.1%
M3528
 
2.1%
A3475
 
2.1%
s3475
 
2.1%
c3456
 
2.0%
w3452
 
2.0%
g3449
 
2.0%
o3444
 
2.0%
Other values (42)134148
79.3%
Common
ValueCountFrequency (%)
=4829
11.1%
03604
 
8.3%
83484
 
8.0%
43422
 
7.9%
23192
 
7.4%
33187
 
7.4%
63161
 
7.3%
13130
 
7.2%
/3095
 
7.1%
+3072
 
7.1%
Other values (3)9176
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII212476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
=4829
 
2.3%
03604
 
1.7%
U3584
 
1.7%
Y3570
 
1.7%
E3543
 
1.7%
M3528
 
1.7%
83484
 
1.6%
A3475
 
1.6%
s3475
 
1.6%
c3456
 
1.6%
Other values (55)175928
82.8%

date
Real number (ℝ≥0)

Distinct679
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20160819.16
Minimum20150126
Maximum20170228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-05-18T18:12:12.726083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20150126
5-th percentile20150828
Q120160321
median20160810
Q320161127
95-th percentile20170211
Maximum20170228
Range20102
Interquartile range (IQR)806

Descriptive statistics

Standard deviation5371.156213
Coefficient of variation (CV)0.0002664155742
Kurtosis0.2159113996
Mean20160819.16
Median Absolute Deviation (MAD)399
Skewness-0.0781400601
Sum9.735659574 × 1010
Variance28849319.07
MonotonicityNot monotonic
2023-05-18T18:12:12.837235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017012225
 
0.5%
2017022323
 
0.5%
2017010421
 
0.4%
2017022820
 
0.4%
2017011320
 
0.4%
2016092320
 
0.4%
2016122520
 
0.4%
2017021919
 
0.4%
2016072719
 
0.4%
2017022419
 
0.4%
Other values (669)4623
95.7%
ValueCountFrequency (%)
201501261
 
< 0.1%
201501281
 
< 0.1%
201501291
 
< 0.1%
201501311
 
< 0.1%
201502072
< 0.1%
201502092
< 0.1%
201502101
 
< 0.1%
201502121
 
< 0.1%
201502142
< 0.1%
201502164
0.1%
ValueCountFrequency (%)
2017022820
0.4%
2017022711
0.2%
2017022614
0.3%
2017022510
0.2%
2017022419
0.4%
2017022323
0.5%
2017022213
0.3%
2017022117
0.4%
2017022012
0.2%
2017021919
0.4%

num_25
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct99
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.998964589
Minimum0
Maximum413
Zeros1196
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:12:13.169493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile27
Maximum413
Range413
Interquartile range (IQR)6

Descriptive statistics

Standard deviation15.93768282
Coefficient of variation (CV)2.277148658
Kurtosis181.012801
Mean6.998964589
Median Absolute Deviation (MAD)2
Skewness10.18108207
Sum33798
Variance254.0097338
MonotonicityNot monotonic
2023-05-18T18:12:13.274464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01196
24.8%
1756
15.7%
2494
10.2%
3374
 
7.7%
4266
 
5.5%
5239
 
4.9%
6157
 
3.3%
7148
 
3.1%
8122
 
2.5%
9107
 
2.2%
Other values (89)970
20.1%
ValueCountFrequency (%)
01196
24.8%
1756
15.7%
2494
10.2%
3374
 
7.7%
4266
 
5.5%
5239
 
4.9%
6157
 
3.3%
7148
 
3.1%
8122
 
2.5%
9107
 
2.2%
ValueCountFrequency (%)
4131
< 0.1%
3301
< 0.1%
3221
< 0.1%
2391
< 0.1%
2221
< 0.1%
1661
< 0.1%
1641
< 0.1%
1411
< 0.1%
1341
< 0.1%
1331
< 0.1%

num_50
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.764340443
Minimum0
Maximum122
Zeros2235
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:12:13.376333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum122
Range122
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.706018186
Coefficient of variation (CV)2.667295989
Kurtosis174.5560379
Mean1.764340443
Median Absolute Deviation (MAD)1
Skewness10.50907629
Sum8520
Variance22.14660717
MonotonicityNot monotonic
2023-05-18T18:12:13.483844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
02235
46.3%
11146
23.7%
2559
 
11.6%
3311
 
6.4%
4170
 
3.5%
5101
 
2.1%
663
 
1.3%
753
 
1.1%
837
 
0.8%
1024
 
0.5%
Other values (37)130
 
2.7%
ValueCountFrequency (%)
02235
46.3%
11146
23.7%
2559
 
11.6%
3311
 
6.4%
4170
 
3.5%
5101
 
2.1%
663
 
1.3%
753
 
1.1%
837
 
0.8%
918
 
0.4%
ValueCountFrequency (%)
1221
< 0.1%
981
< 0.1%
721
< 0.1%
651
< 0.1%
601
< 0.1%
571
< 0.1%
561
< 0.1%
552
< 0.1%
471
< 0.1%
461
< 0.1%

num_75
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.034789812
Minimum0
Maximum68
Zeros2600
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:12:13.577866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum68
Range68
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.085583694
Coefficient of variation (CV)2.015466011
Kurtosis243.032609
Mean1.034789812
Median Absolute Deviation (MAD)0
Skewness10.04576581
Sum4997
Variance4.349659344
MonotonicityNot monotonic
2023-05-18T18:12:13.655399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
02600
53.8%
11157
24.0%
2486
 
10.1%
3247
 
5.1%
4125
 
2.6%
579
 
1.6%
648
 
1.0%
726
 
0.5%
914
 
0.3%
811
 
0.2%
Other values (12)36
 
0.7%
ValueCountFrequency (%)
02600
53.8%
11157
24.0%
2486
 
10.1%
3247
 
5.1%
4125
 
2.6%
579
 
1.6%
648
 
1.0%
726
 
0.5%
811
 
0.2%
914
 
0.3%
ValueCountFrequency (%)
681
 
< 0.1%
301
 
< 0.1%
241
 
< 0.1%
232
 
< 0.1%
211
 
< 0.1%
182
 
< 0.1%
152
 
< 0.1%
142
 
< 0.1%
131
 
< 0.1%
126
0.1%

num_985
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct26
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.137709671
Minimum0
Maximum135
Zeros2537
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:12:13.739698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum135
Range135
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.237794079
Coefficient of variation (CV)2.845887806
Kurtosis803.4933634
Mean1.137709671
Median Absolute Deviation (MAD)0
Skewness22.86078337
Sum5494
Variance10.48331049
MonotonicityNot monotonic
2023-05-18T18:12:13.827449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
02537
52.5%
11160
24.0%
2523
 
10.8%
3239
 
4.9%
4151
 
3.1%
573
 
1.5%
649
 
1.0%
725
 
0.5%
916
 
0.3%
816
 
0.3%
Other values (16)40
 
0.8%
ValueCountFrequency (%)
02537
52.5%
11160
24.0%
2523
 
10.8%
3239
 
4.9%
4151
 
3.1%
573
 
1.5%
649
 
1.0%
725
 
0.5%
816
 
0.3%
916
 
0.3%
ValueCountFrequency (%)
1351
 
< 0.1%
991
 
< 0.1%
482
< 0.1%
421
 
< 0.1%
281
 
< 0.1%
261
 
< 0.1%
241
 
< 0.1%
191
 
< 0.1%
172
< 0.1%
163
0.1%

num_100
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct203
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.31807828
Minimum0
Maximum393
Zeros192
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:12:13.928074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median16
Q335
95-th percentile107
Maximum393
Range393
Interquartile range (IQR)29

Descriptive statistics

Standard deviation36.28610944
Coefficient of variation (CV)1.281376126
Kurtosis9.957715617
Mean28.31807828
Median Absolute Deviation (MAD)12
Skewness2.681760175
Sum136748
Variance1316.681738
MonotonicityNot monotonic
2023-05-18T18:12:14.036131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1239
 
4.9%
4197
 
4.1%
0192
 
4.0%
2187
 
3.9%
3173
 
3.6%
5172
 
3.6%
8157
 
3.3%
7147
 
3.0%
6131
 
2.7%
10127
 
2.6%
Other values (193)3107
64.3%
ValueCountFrequency (%)
0192
4.0%
1239
4.9%
2187
3.9%
3173
3.6%
4197
4.1%
5172
3.6%
6131
2.7%
7147
3.0%
8157
3.3%
9121
2.5%
ValueCountFrequency (%)
3931
< 0.1%
3181
< 0.1%
3121
< 0.1%
2741
< 0.1%
2591
< 0.1%
2521
< 0.1%
2461
< 0.1%
2451
< 0.1%
2402
< 0.1%
2281
< 0.1%

num_unq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct188
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.0581901
Minimum1
Maximum436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:12:14.149888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median18
Q338
95-th percentile95
Maximum436
Range435
Interquartile range (IQR)30

Descriptive statistics

Standard deviation32.72668902
Coefficient of variation (CV)1.126246642
Kurtosis12.49604329
Mean29.0581901
Median Absolute Deviation (MAD)13
Skewness2.683394964
Sum140322
Variance1071.036174
MonotonicityNot monotonic
2023-05-18T18:12:14.252237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1205
 
4.2%
4180
 
3.7%
3174
 
3.6%
2172
 
3.6%
5172
 
3.6%
8159
 
3.3%
6134
 
2.8%
11134
 
2.8%
7127
 
2.6%
12120
 
2.5%
Other values (178)3252
67.3%
ValueCountFrequency (%)
1205
4.2%
2172
3.6%
3174
3.6%
4180
3.7%
5172
3.6%
6134
2.8%
7127
2.6%
8159
3.3%
9119
2.5%
10114
2.4%
ValueCountFrequency (%)
4361
< 0.1%
3121
< 0.1%
2951
< 0.1%
2801
< 0.1%
2431
< 0.1%
2201
< 0.1%
2181
< 0.1%
2131
< 0.1%
2081
< 0.1%
2061
< 0.1%

total_secs
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3405
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite7
Infinite (%)0.1%
Meannan
Minimum-inf
Maximuminf
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size9.6 KiB
2023-05-18T18:12:14.356974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-inf
5-th percentile303.25
Q11805
median4408
Q39552
95-th percentile27369.6
Maximuminf
Rangeinf
Interquartile range (IQR)7747

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meannan
Median Absolute Deviation (MAD)3170
Skewnessnan
Sumnan
Variancenan
MonotonicityNot monotonic
2023-05-18T18:12:14.457282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
inf6
 
0.1%
20726
 
0.1%
49685
 
0.1%
100725
 
0.1%
41365
 
0.1%
23905
 
0.1%
49165
 
0.1%
140325
 
0.1%
102325
 
0.1%
65445
 
0.1%
Other values (3395)4777
98.9%
ValueCountFrequency (%)
-inf1
< 0.1%
1.8017578121
< 0.1%
2.5332031251
< 0.1%
2.894531251
< 0.1%
4.011718751
< 0.1%
4.457031251
< 0.1%
4.964843751
< 0.1%
6.949218751
< 0.1%
7.175781251
< 0.1%
9.46093751
< 0.1%
ValueCountFrequency (%)
inf6
0.1%
618881
 
< 0.1%
608961
 
< 0.1%
605441
 
< 0.1%
561601
 
< 0.1%
557761
 
< 0.1%
548481
 
< 0.1%
539521
 
< 0.1%
538241
 
< 0.1%
524161
 
< 0.1%

Interactions

2023-05-18T18:12:04.471732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:04.558323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:04.651741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:04.734369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:04.820381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:04.907898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:04.995863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.085525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.168299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.258524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.355431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.458978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.553377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.650552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.749030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.848058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:05.948459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.042426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.140423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.221791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.311135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.389912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.472038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.555961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.640401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.726485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.805289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.888635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:06.975374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.069878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.153834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.241171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.329955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.419523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.510558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.594524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.683300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.772425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.869460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:07.957459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.047702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.139158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.231271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.325099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.411788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.503249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.592167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.689327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.776286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:08.866189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.176206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.274004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.368152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.455466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.547141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.638936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.739019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.828511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:09.921403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.016291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.111549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.208795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.298410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.384012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.464671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.557152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.639620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.722215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.806148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.890035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:10.975991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.054540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.138259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.225577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.320636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.405882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.494789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.585059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.675664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.762351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:12:11.847739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-18T18:12:14.546691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-05-18T18:12:14.659693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-05-18T18:12:14.771462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-05-18T18:12:14.884691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-05-18T18:12:12.001434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-18T18:12:12.152158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexmsnodatenum_25num_50num_75num_985num_100num_unqtotal_secs
047813ZMqqE8u7R/12bWzAViCUlfyEZHrcw80XEsrQHU+dZUg=20160923231120265612.0
1610745MDBdrLTDMSW6N5HBrQ5zMINuP+40NtsNIaczfyZk//M=201612160010721565.0
2662811nXhDC780WU3nOGfeCjSmvHu2LtVD3MpJQjGljN7lxvg=20160209141007201997.0
3801517K5tYghuu/DYjBZZaH+hfT3hOo+Oq/SSQ08A7+EgjWq4=201605312010461032.0
42001312AwebbTh1Flk5wlsSVzpqSt3b0CSdqemjXv53zdhiqWc=20160919300013162944.0
54807528cpj7Ozph04uqd82G4+y7mlIizoMRAJVq+7Dr3g4Tiak=201701105101261185.0
6162521/LQQ2BhoSMYQwfMzhfqExl1QO/wVpyEEaM4VKE69lBw=20160608310122275884.0
72602174m/cb+oHs01UyIl7NKsz5UODg3LdP4OPiYHOOCE8aJzw=20160724712211408913208.0
84572800kFxaXeLUTCaDcfrFYsswCcRDzcJ4yv4G0GIOi9+8XQk=20160815441003401528.0
94392681izbJFXkVseLFSo88hYbeNvzebivlVb3O2eZmHonJ6Ds=201702121221846120800.0

Last rows

df_indexmsnodatenum_25num_50num_75num_985num_100num_unqtotal_secs
4819730038L6GXAIW/sG8eTsJRRSjMjWstldMzztJ4mdY5C1sx38s=2015092870111133329184.0
48203176937fs5aPuroxK1X0EIPp1Y+lIDxXSb40pTI6isxjhg3WKw=20170204600135713.5
4821586878IPzfhGbM+ze2M67t+uW9Cy77NbSWTPJSriMxJO4JD4k=20160105000028286816.0
48221454964X7W92ku30OKsXA/z+318ZVtv5VZO2SINV1pIMr6IJOc=201601131724127364284.0
48232919035eClquUEzNz8F+JdCbfEs9/aCw0wuhyoWQT3GMqMF6Nc=2016040351005111220.0
4824342522K910kzvQO5IkkCo4cesBF2I0FHSxsvpcbZICrmevxEU=2015120516200516413688.0
48252533079iGfPwHz9KLibjU9pGGvR0quT887+b0OyBAVX6sw7CyY=201610024442215584804.0
48264161858uext6x4kOlP/we/gaAu4q4dLPSPHo5ODHkD8Hsw1xZA=201508041001022275732.0
48271359366/Ib3hKp0WET3C7tCjv4mFKEV2i1EacuTcs0nocTosko=201510281202271043.0
482812668414QRW4r1vzS9pXyY15vJjJMQw9OlHJTv21VBRviHnd/o=2016021242423608216784.0